Device-free localization methods within smart indoor environments

ABSTRACT

Device-free localization for smart indoor environments within an indoor area covered by wireless networks is detected using active off-the-shelf-devices would be beneficial in a wide range of applications. By exploiting existing wireless communication signals and machine learning techniques in order to automatically detect entrance into the area, and track the location of a moving subject within the sensing area a low cost robust long-term tracking system can be established. A machine learning component is established to minimize the need for user annotation and overcome temporal instabilities via a semi-supervised framework. After establishing a robust base learner mapping wireless signals to different physical locations from a small amount of labeled data; during its lifetime, the learner automatically re-trains when the uncertainty level rises significantly. Additionally, an automatic change-point detection process is employed setting a query for updating the outdated model and the decision boundaries.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority as a continuation ofU.S. patent application Ser. No. 17/019,759 filed Sep. 14, 2020; whichitself claims the benefit of priority as a continuation of U.S. patentapplication Ser. No. 16/461,492 filed May 16, 2019 which has issued asU.S. Pat. No. 10,779,127; which itself claims priority as a 371 NationalPhase Application of PCT/CA2017/000,247 filed Nov. 21, 2017; whichitself claims priority from U.S. Provisional Patent Application62/425,267 filed Nov. 22, 2016; the contents of each being incorporatedherein by reference.

FIELD OF THE INVENTION

This invention relates to localization and more particularly to systems,methods, and data processing apparatus for long-term and robustdevice-free localization in smart indoor spaces.

BACKGROUND OF THE INVENTION

Positioning or indoor localization is an essential function of a smartenvironment, which enables discovering valuable knowledge about theperformances, behaviour and preferences of residents, especially thosewho need long-term monitoring or care. Moreover, location-basedapplications that utilize such information can offer customizableservices according to the dynamics of their users' surroundings.Surveillance and security, health and sleep monitoring, assisted livingfor elderly people or patients with disabilities and entertainment are afew examples of applications wherein indoor location-aware computing hassignificantly improved performance.

Generally, there are two different categories of indoor localizationsystems based on how their sensing infrastructure interacts with thetarget: device-based and device-free. Most approaches within the priorart exploit device-based systems, where the location of a moving targetor human body within the space is determined and represented by a deviceassociated with the moving target or human user such as a Wirelessenabled smart phone or a radio-frequency identification (RFID) tag.

These technologies are usually accurate and reliable, but most of themsuffer from practical issues such as privacy concerns, physical contactwith sensors, high implementation and maintenance cost, and cooperationfrom the subjects. Conversely, device-free passive (DFP) approaches donot require users to carry any devices or actively participate in thepositioning process. Most of the DFP localization systems adopt a radiofrequency (RF) sensing infrastructure (such as RFID, microwave, FMsignals, etc.) and rely on the influence of the human body's presenceand movement to influence these signals, e.g. through reflection.

A few existing systems have employed information gleaned from Wirelesssignals such as channel state information (CSI) and received signalstrength indicator (RSSI) to perform active or passive localizationindoors. These systems are mainly enabled by recent wireless technologyimprovements and the fact that wireless signals are pervasive at most ofindoor spaces such as residential, industrial, and public places. Thebasic idea amongst such systems is to take advantage of these wirelesssignals to monitor and quantify the distortions arising in the strengthand patterns of signals between two nodes of communication (transmitterand receiver) and characterize the environment including human movementsand their locations. See, for example, Xiao et al. in “PassiveDevice-Free Indoor Localization using Channel State Information” (Proc.IEEE 33^(rd) Int. Conf. Distributed Computing Systems (ICDCS), pp.236-245, 2013) wherein a CSI-based localization system utilizes multiplepairs of transmitter-receiver devices to estimate the location of amoving entity within a sensing area.

Despite some preliminary success, most of these device-free passivesystems have been implemented and evaluated using several devices incontrolled sensing environments, such as a university laboratory or aclassroom, with a large volume of human annotated data and withinpredefined and short-term scenarios.

On the other hand, wireless signal components are sensitive to manyinternal and external factors including but not limited to multi-pathinterference, building attenuation, device and/or antenna orientationissues, changes in the environment (such as changing the position ofobjects) and signal interference. Therefore, performance of suchlocalization systems usually degrades under realistic conditions and/orover time.

Accordingly, it would be beneficial to provide a system that offers arobust and passive solution for inferring the location of a movingtarget within an indoor sensing area, which can be created by (at least)a pair of off-the-shelf wireless devices. Furthermore, it would bebeneficial for the system to exploit a semi-supervised learningframework employing multiple machine learning techniques in order forthe system to maintain long-term accuracy and performances.

SUMMARY OF THE INVENTION

It is an object of the present invention to mitigate limitations withinthe prior art relating to localization and more particularly to systems,methods, and data processing apparatus for long-term and robustdevice-free localization in smart indoor spaces

In accordance with an embodiment of the invention there is provided asystem for moving target localization within indoor environments coveredby existing wireless communication infrastructure, wherein the systemdivides the localization problem into two phases comprising an initialoffline training phase using a batch of labeled training data, (i.e.wireless signals and their corresponding location labels) and an onlineevaluation and adaptation phase using the unlabeled streaming data (i.e.wireless signals without their any associated location labels).

In accordance with an embodiment of the invention there is provided amethod for establishing an initial offline location recognition phasethat includes receiving and analyzing wireless signals and theircorresponding labels, while a user is present within different locationspots of a sensing environment. The method comprising various signalprocessing, data mining and feature extraction techniques tostatistically formulate the correlation between wireless signal readingsand the location of the movements and events inside the sensing area.The method also includes building a probabilistic localization modelusing a base classifier, which utilizes computed statistics fromwireless signals to generate respective decision boundaries and aconfidence score that quantifies how certain the classifier is of itsdecision.

In accordance with an embodiment of the invention there is provided amethod for real-time localization phase comprising receiving a livestream of unlabeled wireless signals without any associated locationindication and estimating a location label for each segment of wirelesssignals using a probabilistic model built in an initial offline trainingphase. The method also includes a decision-making module that appliesseveral strategies, for reducing the variance in the sequence ofpredicted labels and hence improving the stability of the localizationsystem. The method also includes outputting final location labels toanother system and/or process.

In accordance with an embodiment of the invention there is provided amethod for automatically detecting any structural shift and/or drift inthe distribution of the streaming data comprising receiving a livestream of unlabeled wireless signals, applying a change-point-detectiontechnique by continuously computing a divergence score, identifying thesignificant changes having a score above a predefined threshold. Themethod also includes outputting indicator of drift to another systemand/or process.

In accordance with an embodiment of the invention there is provided amethod for adapting the decision boundaries of a base classifier afteroccurrence of a shift/drift in wireless signal data stream is determinedcomprising receiving a stream of confidence scores and predicted labelsfrom the real-time localization process and receiving at least one driftindicator. The method triggers an active query system that includesreceiving the indicator, creating a repository of high-confidencesamples of the wireless signals and their corresponding predicatedlabels and updating the training data of the base classifier accordingto the new changes.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described, by way ofexample only, with reference to the attached Figures, wherein:

FIG. 1 depicts an exemplary system overview of an intelligentlocalization system according to an embodiment of the invention;

FIG. 2 depicts an exemplary schematic representation of signalpropagation between transmitter and receiver antennas within a sensingarea according to an embodiment of the invention;

FIG. 3 depicts typical measurements of CSI signal magnitudes obtainedwhilst a user is walking insides two different sub-regions within asensing area according to an embodiment of the invention;

FIG. 4 depicts an exemplary architecture of a system for featuregeneration from the CSI measurements according to an embodiment of theinvention;

FIG. 5 depicts an exemplary architecture of the proposed methodology foradaptive localization framework according to an embodiment of theinvention;

FIG. 6 depicts an example of device placement within a residentialapartment; and

FIG. 7 depicts an exemplary performance evaluation of accuracy of theroom-level location identification concept according to an embodiment ofthe invention at different time intervals with and without adaptivesolution.

DETAILED DESCRIPTION

The present invention is directed to localization and more particularlyto systems, methods, and data processing apparatus for long-term androbust device-free localization in smart indoor spaces.

The ensuing description provides representative embodiment(s) only, andis not intended to limit the scope, applicability or configuration ofthe disclosure. Rather, the ensuing description of the embodiment(s)will provide those skilled in the art with an enabling description forimplementing an embodiment or embodiments of the invention. It beingunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims. Accordingly, an embodiment is anexample or implementation of the inventions and not the soleimplementation. Various appearances of “one embodiment,” “an embodiment”or “some embodiments” do not necessarily all refer to the sameembodiments. Although various features of the invention may be describedin the context of a single embodiment, the features may also be providedseparately or in any suitable combination. Conversely, although theinvention may be described herein in the context of separate embodimentsfor clarity, the invention can also be implemented in a singleembodiment or any combination of embodiments.

Reference in the specification to “one embodiment”, “an embodiment”,“some embodiments” or “other embodiments” means that a particularfeature, structure, or characteristic described in connection with theembodiments is included in at least one embodiment, but not necessarilyall embodiments, of the inventions. The phraseology and terminologyemployed herein is not to be construed as limiting but is fordescriptive purpose only. It is to be understood that where the claimsor specification refer to “a” or “an” element, such reference is not tobe construed as there being only one of that element. It is to beunderstood that where the specification states that a component feature,structure, or characteristic “may”, “might”, “can” or “could” beincluded, that particular component, feature, structure, orcharacteristic is not required to be included.

Reference to terms such as “left”, “right”, “top”, “bottom”, “front” and“back” are intended for use in respect to the orientation of theparticular feature, structure, or element within the figures depictingembodiments of the invention. It would be evident that such directionalterminology with respect to the actual use of a device has no specificmeaning as the device can be employed in a multiplicity of orientationsby the user or users.

Reference to terms “including”, “comprising”, “consisting” andgrammatical variants thereof do not preclude the addition of one or morecomponents, features, steps, integers or groups thereof and that theterms are not to be construed as specifying components, features, stepsor integers. Likewise, the phrase “consisting essentially of”, andgrammatical variants thereof, when used herein is not to be construed asexcluding additional components, steps, features integers or groupsthereof but rather that the additional features, integers, steps,components or groups thereof do not materially alter the basic and novelcharacteristics of the claimed composition, device or method. If thespecification or claims refer to “an additional” element, that does notpreclude there being more than one of the additional element.

A “personal electronic device” (PED) as used herein and throughout thisdisclosure, refers to a wireless device used for communications and/orinformation transfer that requires a battery or other independent formof energy for power. This includes devices such as, but not limited to,a cellular telephone, smartphone, personal digital assistant (PDA),portable computer, pager, portable multimedia player, remote control,portable gaming console, laptop computer, tablet computer, and anelectronic reader.

A “fixed electronic device” (FED) as used herein and throughout thisdisclosure, refers to a device that requires interfacing to a wired formof energy for power. However, the device can access one or more networksusing wired and/or wireless interfaces. This includes, but is notlimited to, a television, computer, laptop computer, gaming console,kiosk, terminal, and interactive display.

The term “wireless” as used herein and throughout this disclosure,refers to wireless communication which transfers information or powerbetween two or more points that are not connected by an electricalconductor. The most common wireless technologies use radio waves tocarry information by systematically modulating some property ofelectromagnetic energy waves transmitted through space, such as theiramplitude, frequency, phase, or pulse width. Within this specificationthe term is employed with respect to systems, networks, devices,protocols etc. which may operate according to one or more standardsincluding, but not limited to, international standards, nationalstandards, standards established and maintained by an alliance ofenterprises, and a standard established by an enterprise or small groupof individuals. Whilst the embodiments of the invention are primarilydescribed with respect to wireless infrastructure exploiting OrthogonalFrequency Division Multiplexing (OFDM) and accordingly infrastructurewhich may include that exploiting, but not limited to, wireless LAN(WLAN) such as IEEE 802.11a, IEEE 802.11g, IEEE 802.11n, IEEE802.11ac,HiperLAN/2 etc.; digital radio systems such as DAB, DAB+, etc.; digitaltelevision systems such as DVB-T, DVB-H, etc.; OFDM multiple access(OFDM-MA) such as IEEE 802.16e, IEEE 802.20, 3GPP long term evolution(LTE) etc.

The system described below in respect of FIGS. 1 to 7 is an intelligentindoor localization framework in the broader context of smart indoorspaces. This design methodology is motivated by low cost wirelesstechnology that allows capturing and monitoring of the influence ofhuman movements and/or any moving target (e.g., pet, robot) on thewireless signal propagation within indoor spaces covered by wirelessnetworks. Owing to these recent improvements, the collected measurementsfrom a range of existing off-the-shelf wireless devices such as laptopcomputers, smart TVs, wireless routers, and wireless access points, havegreat potential to reveal detailed information about the source(s) ofthe movements in the active sensing environment without requiring theinstallation and management of substantial dedicated hardware.Accordingly, embodiments of the invention address long-term indoorlocalization within residential, commercial, retail, and otherenvironments either without any dedicated excessive device requirementsor through the deployment of wireless infrastructure that does notrequire significant labour, expense, modification of the property.

In modern wireless communications, a wireless signal, including but notlimited to channel state information (CSI), propagates between atransmitter and receiver through multiple transmission channels usingOrthogonal Frequency Division Multiplexing (OFDM). This means thatwithin each channel, the transmitter broadcasts simultaneously onseveral narrowly separated sub-carriers at different frequencies inorder to increase the data rate. One example of the wirelessmeasurements regarding channel properties which can form the basis ofembodiments of the invention are the Channel State Information (CSI)values which can be obtained at the receiver. These describe how thetransmitted signal is propagated through the channel and reveal channelvariations and signal distortions experienced during propagation caused,for example, by scattering, fading and power decay with distance. Thequantitative analysis of this signal propagation behavior within awireless-covered area can identify and measure different types ofdisturbances, including those relating to human motion and the locationof the movement.

The real-time framework according to embodiments of the invention isintended to discover the location of a human target within a sensingarea at the sub-region level, by continuously collecting wirelesssignals and applying several analytic and modeling procedures to thecollected measurements in order to infer correlation between obtainedmeasurements and the location of the movements. A sub-region within asensing area is defined as any smaller division or subdivision of alarger indoor area, which may (or may not) also align with the roomboundaries within the property, such as kitchen, bedroom, living roomand dining room within a residential property.

There are two major technical challenges that need to be addressed inorder to design a robust wireless-based localization system. First,finding mathematical characterization of the disturbance and changes inwireless signals, originated from human body movements, is a challengingproblem due to the complexity of the wireless signal propagation inindoor environments. Therefore, the first challenge of designing anintelligent indoor localization system is to characterize statisticallythe correlation between the location of motions and the signals.

Moreover, in practice, many undesired environmental and/or internalconditions can cause temporal instability and high variance in wirelesssignals, which may result in shift or drift in the localization modellearned from these measurements. The unwanted changes degrade theperformance and accuracy of the localization system over time bystrongly affecting the correlation between input measurements and theinferred locations.

A general exemplary system overview of the proposed intelligentlocalization system according to an embodiment of the invention isdepicted in FIG. 1 . The proposed system offers robust long-termlocalization by operating in two phases. Within the first phase, anOffline Training procedure 400 exploits a small set of wireless dataassociated with corresponding sub-region labels, Labelled Training Data300, to build an initial base classifier for location identification. Inthe second phase, an Online Evaluation phase 600, the location of amoving object within the sensing area is calculated from UnlabeledStreaming Data 500 and using the base localization model. The OnlineEvaluation phase 600 also includes an automatic procedure to detectfundamental changes in the signal structures, and an adaptivedecision-making module including strategies and solutions to cope withthe noisy and non-stationary nature of wireless signals.

Additionally, the wireless measurements collected from an active sensingarea are initially processed in a Data Preparation module 200, wheremultiple steps of pre-processing and data mining procedures are carriedout, in order to eliminate or reduce redundant and noisy samples andprepare a stable feature vector before providing it to the localizationsystem. The data preparation module includes, but is not limited to,Noise Removal 210, Standardization/Data Preparation 220, and FeatureAcquisition 230 units. The Data Preparation module 200 exploits the dataacquired/generated within Wireless Sensing Area Output 100 which areacquired by the wireless devices within the sensing area such asportable electronic devices (PEDs) and/or fixed electronic devices(FEDs). An infrastructure exploiting FEDs is subject to less frequentadjustment/change by addition and/or removal of another FED whereas PEDsmay be more variable in their presence and/or location.

Having outlined in FIG. 1 the main blocks and their role in the systemaccording to an embodiment of the invention further details regardingeach specific module are described and discussed below in respect ofsome implementation examples.

Referring to FIG. 2 there is depicted a Sensing Area 110 created by ann×m multiple input and multiple output (MIMO) system with n transmittingantennae and m receiving antennae. Accordingly, FIG. 2 depicts aschematic representation of signal propagation between a pair ofwireless devices. Let

∈{1, . . . , L} denote the antenna links between Transmitter 1 andReceiver 1, where L=n×m, and

(t) denote a complex number describing the signal received at subcarrieri ∈{1, . . . , I} at time t, which is defined by Equation (1).

$\begin{matrix}{{CSI}_{i\;\ell} = {{{CSI}_{i\;\ell}}e^{{- j}\mspace{14mu}{\sin\angle}\;{CSI}_{i\;\ell}}}} & (1)\end{matrix}$

| and ∠

denote the amplitude response and the phase response of subcarrier i oflink

, respectively. The total number of subcarriers I per link depends onthe physical property of the hard-ware device used for collecting CSIvalues and is fixed for all links.

Environmental changes and human body movements affect the CSI values ofdifferent links independently, but affect the different subcarriers ofeach link in a similar manner. FIG. 3 presents examples of CSI amplitudestreams captured over 20 seconds from two different links, while a useris walking inside two different sub-regions, Room 1 and Room 2, of aresidential apartment, as well as a capture from an empty sensing areawith no motion. Accordingly, FIG. 3 depicts first CSI data 310 for emptysensing area with link 1; second CSI data 320 for room 1 with link 1 anduser moving; third CSI data 330 for room 2 with link 1 and user moving;fourth CSI data 340 for empty sensing area with link 2; fifth CSI data350 for room 1 with link 2 and user moving; and sixth CSI data 360 forroom 2 with link 2 and user moving.

As mentioned supra, the collected CSI measurements are constantlytransformed from the sensing device to a Data Preparation module 200,where multiple processing procedures are applied to the data streams toenhance the raw data for further analysis, and to extract and/orgenerate discriminative features that precisely reflect distinguishableproperties of different sub-regions within the sensing area. Asdiscussed supra Data Preparation module 200 comprises but is not limitedto, a Noise Removal 210 unit, Standardization 220 unit and FeatureAcquisition 230 units.

Noise Removal 210: The raw data contain high-frequency noise from avariety of internal and surrounding sources. Moreover, the mobility andother physical activities of human or any moving target within indoorspaces happen at different but predictable range of frequencies.Therefore, a set of digital filters targeting specific frequency bandscollect information about different target moving activities, such as ahuman walking or pet movement, is considered as part of the methodsdescribed herein. As a working example, the frequency of typical humanwalking happens at low frequency, for example below 2 Hz, andaccordingly a low-pass filter with cut-off frequency of 2 Hz can beapplied to each CSI stream individually, in order to remove thehigh-frequency noise as well as the static components.

Standardization 220: At each time stamp t, multiple CSIs values fordifferent Transmitter-Receiver links can take values in differentdynamic ranges, while the values of different subcarriers within eachlink can get shifted and scaled over time. These irrelevant and unwantedvariations can be removed by introducing a fixed-score scalingnormalization module, for example, which standardizes the CSI featurespace to a predefined reference range, such that meaningful and desiredvariations in the signals can be reliably tracked. The L2-norm of theCSI vector was calculated for each link to rescale all values to thereference range.

Feature Acquisition 230: Extracting relevant features from the inputdata helps to explore frequency diversity of CSI values with differentamplitudes and phases over multiple subcarriers and their correlation tothe events occurred in the covered area. The Feature Acquisition module230 begins by sliding a moving window with overlap over the stream ofsamples, in order to extract correlated features that describe thelocation of environmental events. This creates a vector of the formgiven by Equation (2).

$\begin{matrix}{{W(t)} = \left\{ {{{CSI}_{i\;\ell}\left( {t - w + 1} \right)},\ldots\mspace{14mu},{{CSI}_{i\;\ell}\left( {t - 1} \right)},{{CSI}_{i\;\ell}(t)}} \right\}} & (2)\end{matrix}$

Here, w is the size of the moving window and t is the time stamp of theCSI values of subcarrier i of link

. As introduced supra, complex values

can be presented by their Amplitude Information 230A |

|, and Phase Information 230B ∠

. Subsequently, this data is used to extract/generate a new featuresspace with the fusion of multiple domain information including, but notlimited to, time-domain or Temporal Amplitude Information 231, FrequencyAmplitude Information 232, and Phase Information 233.

Referring to FIG. 4 , some exemplary strategies for Feature Acquisition230 are illustrated. As a working example, the following statistic arecalculated within an embodiment of the invention to correlate the CSIsignal behavior to the location of movements in the Sensing Area 110.

Temporal Amplitude Information 231: Statistics computed over time fromper-subcarrier CSI amplitudes, are the most widely used features inCSI-base systems, since they exhibit higher temporal stability. Withinembodiments of the invention, the moving variance and moving average ofall CSI amplitudes within each moving window

(t) are extracted, following the same feature extraction techniquesintroduced by the inventors in U.S. Provisional Patent 62/347,217entitled “System and Methods for Smart Intrusion Detection usingWireless Signals and Artificial Intelligence” filed Jun. 8, 2016.

Frequency Amplitude Information 232: Various CSI amplitudes fordifferent subcarriers of each Rx-Tx link describe channel properties inthe frequency domain and a moving subject can change signal reflectionsdifferently based on their location. This results in different delayprofiles, where the frequency information is embedded in thecorrelations among (CSI values of) subcarriers in each Rx-Tx link.Within an embodiment of the invention, the frequency information isinferred by computing statistics within each moving window

(t) that include, but are not limited to, variance, log energy entropy,standard deviation, kurtosis, and skewness.

Temporal Phase Information 233: In wireless communication applications,the phase difference between the received signals at each antenna arrayis roughly correlated to the angle of arrival (AOA), which yields amethod for determining the direction of RF wave propagation. Throughexploratory experimentation, the inventors established that the phasedifferences between various pairs of Rx-Tx links can help localize humanmovement with respect to the positions of the transmitter and receiverdevices. Therefore, within some embodiments of the invention, thevariance of the phase differences between the subcarriers of all pairsof Rx-Tx is tracked over each moving window

(t), as another group of relevant features for the proposed localizationsystem.

The Data Preparation module 200 is followed by the proposed localizationsystem, where machine learning and decision-making techniques are usedto infer the location of the moving target within the sensing area. Anexemplary architecture of the proposed methodology for adaptivelocalization framework according to an embodiment of the invention isdepicted in FIG. 5 .

The localization process initiates by transforming a small amount ofprepared CSI data from step 500, associated with correspondingsub-region labels from Initial Location Annotations 800 unit to form aTraining Data Pool 310. The Offline Training process 400 begins byfitting a base supervised learner (Base classifier 410) to obtain amapping between features extracted from CSIs and different sub-regionsin the sensing environment, using the initial labeled data. In order tosimplify the problem, localization is performed at the level of discrete“sub-regions” inside a dwelling, which could be rooms, but also finergrained than rooms (“on the couch” or “in the reading chair”, forexample).

An example of algorithm that can be used as the Base Classifier 410 foridentifying the location of a walking subject is “Random Forests”, seeBreiman in “Random Forests” (Machine Learning, Vol. 45(1), pp. 5-32,2001), although it would be evident that other classification techniquesas known within the art may be applied. Random Forest is an ensembleestimator that builds several decision trees on random subsets of thesamples from the original training set and then aggregates theirindividual predictions, usually by averaging, to form final decisions.Therefore, besides predicting a label, the obtained classifier alsoprovides a measure of the uncertainty in its prediction, expressedthrough the proportion of votes given by all trees for each class. Thus,the proportion of votes that agree on the outcome can be used toestimate a probabilistic confidence score, which quantifies how certainthe classifier is of its decision.

After building the base classifier, Online Evaluation 600 which runsreal time on the streaming data begins. Arriving CSI measurements areprocessed in Data Preparation module 200 as described before, and theobtained features are then fed into the Base Classifier 410 frame byframe, which results into a stream of predicted sub-region labels(transferred to Location Prediction 610 unit), associated with theircorresponding confidence scores (transferred to Confidence Score 620unit).

From a practical point of view, it is important to have a stablelocalization system, which smoothly transits between different locationclasses when the user walk inside the sensing area from one sub-regionto another. Thus, in order to reduce the variance in the sequence ofpredicted labels and minimize the error when outputting final decisionsto the end user, some additional strategies may be required to increasethe stability of the real-time localization.

Decision Making Strategies module 630: The role of this module is toreceive a buffer of labels from Location Prediction 610 and theirassociated from Confidence Score unit 620, and apply several strategiesto output a stable location label to Localization Model unit 700.

Consider a K-Class classification problem, where for each time frameW(t) (from Equation (2)) a class label c_(t) is independently obtainedfrom the base learner with confidence scores (prediction probability) ofp_(t). Considering a decision frame {acute over (W)}≥W with length{acute over (w)}, where given a prediction history,{c_(t−{acute over (w)}+1), . . . , c_(t−1), c_(t)} and{p_(t−{acute over (w)}+a), . . . , p_(t−1), p_(t)}, a final classdecision C_(T) is made for time buffer T={t−{acute over (w)}+1, . . . ,t−1,t} through several steps, including but not limited to:

-   -   Outlier Removal: discarding rare class labels that last less        than α consecutive samples;    -   Uncertainty Removal: discarding any class label with confidence        score less than β;    -   Transition Bias: imposing an extra bias towards keeping the        current predicted class label until the average confidence score        for switching to another class reaches a certain level γ.

The parameters of Decision Making Strategies 630 module (α, β and γ) canbe empirically learnt from the data over time. At the end of thelocalization process, only the final decision C_(T) is transferred tothe Localization Model 700 unit, where the final predicted locationlabel appears on the user interface.

Concept Drift Detection 640: As mentioned supra, drifts or unwantedchanges in the distribution of input data are expected with long-termusage of the CSI-based localization system. Therefore, the LocalizationModel 700 learnt from the initial training data utilizing the userannotated data needs to be updated over the lifetime of the system. Onepossible solution is to ask the end-user to provide a new batch oflabeled data and retrain the system when the performance of the systemdegrades. However, it is cumbersome to query the end-user too often orany time a drift happens, and the goal is to avoid involving the userfor as long as possible.

In order to maintain the performance of the system in spite of thedrift, the first step is to automatically detect significant changes inthe distribution of features extracted from the CSI stream in a timelymanner, and then update the outdated mode.

The gradual or abrupt drifts happens to the distribution of CSImagnitude (i.e., frequency information) over each Rx-Tx linkindependently, and can affect one, some or all of these links over time.Therefore, a change-point detection algorithm is required to constantlyestimate and monitor the stability of all links individually. Referringto FIG. 5 , a Change-Point Detection module 640 is employed, which usesKullback-Leibler (KL) divergence as a distance metric to tracksubstantial changes in the distribution of the features, {

, . . . , |

|}, although it would be evident that other distance and/or divergencemetrics as known within the art may be applied.

$\begin{matrix}{{{D_{\ell}(\delta)} = {\sum\limits_{i = 1}^{I}\;{{{CSI}_{i\;\ell}(t)}\log\frac{{CSI}_{i\;\ell}(t)}{{CSI}_{i\;\ell}\left( {t + \delta} \right)}}}},} & (3)\end{matrix}$

The KL-divergence between two distributions

(t) and

(t+δ) is estimated by Equation (3) where

corresponds to the drift measure of link

, at time stamp δ after the initial training set captured at time t. Anempirical threshold θ is set to automatically detect any significantdivergence in any element of vector

(δ)={D₁, . . . , D_(L)}. Once a significant drift in any of the links isdetected, the Change-Point Detection module 640 sends a drift indicationto the Active Query System 650 which initiates an automatic update ofthe current Localization Model 700.

Adaptive Localization: Although the unwanted changes in CSI magnitudeand their timing are not predictable, they usually happen over a shortperiod of time and do not involve all signals simultaneously. Therefore,many samples still get correctly classified even after drift hasoccurred, as some partial mappings between the feature space and classlabels still hold. The proposed methodology according to embodiments ofthe invention aims to use a selection of high quality representativesamples from the history to update the Training Data Pool 310. In thismanner the system exploits confidence scores provided by the BaseClassifier 410 to establish high confidence intervals over the stream ofunlabeled data and accumulate a batch of the most representative samplesand their associated inferred labels over time. When the Change-PointDetection module 640 identifies a significant drift that triggersretraining, a query for updating the base classifier is formed. TheActive Query system 650 receives these demands and pushes sub-samplesfrom the most recent High Confidence Intervals 660 into a pool oflabeled training data, Training Data Pool 310. In this way, there is noneed to query the user to avoid deterioration in prediction accuracy andthe system can maintain its performance even after drifts.

Let X={X(1), X(2), . . . , X({acute over (t)}), . . . } be the stream offeatures extracted from CSI values, and let Y={Y(1), Y(2), . . . ,Y({acute over (t)})} be the true labels of X(t): t ∈ {1, . . . , {acuteover (t)}}. A sliding window P of length μ>>w over the streamingunlabeled data starting from t>{acute over (t)}+1, in which a history ofprediction labels {c_(t−μ+1), . . . , c_(t−1), c_(t)}, and confidencescores {p_(t−μ+1), . . . , p_(t−1), p_(t)}, is kept.

The system narrows the collection of samples by setting a relativelyhigh confidence threshold. Shortly after the Change-Point Detectionmodule 640 produces an alert, the system queries the samples in highconfidence intervals and updates the Base Classifier 410 with a fusionof the original training data and these new high-confidence samples. Asthe real-time platform needs to provide long-term functionality, thesize of this repository of samples should be kept in check, in order toprovide good scalability of data storage and retrieval. Thus, the systemensures that the size of the pool does not exceed a certain point.

Operation in Real Environments: The proposed localization platform canoperate in indoor spaces such as residential, commercial, retail, andother environments, using a pair of off-the-shelf wireless devices. Forexample, a mini PC equipped with the CSI collection tool as receiver anda commercial access point as transmitter can be used to create awireless connection for room-level localization. FIG. 6 depicts anexample of device placement and floor plan in one residential apartmentwherein a single Tx 610 and Rx 620 are depicted. After device placement,a short period of initial training is performed by asking the user tosimply walk inside each room (or any other sub-regions within the space)and record the location labels. Also, a capture from the empty apartmentis needed for the “No Motion” class. The user can interact with thesystem through a user interface, which can be accessed from any wirelessenabled device such as a computer or a portable device such as tablet orsmartphone. Once the initial training is over, the real-timelocalization system is activated, and the user is able to track thelocation of a moving person within the apartment. Beside locationidentification, this tool can be used as an intruder alarm that notifiesthe user as soon as a person enters in their empty apartment.

Performance Evaluation: The initial training of the localization systemusually results in very robust performance, but it only lasts about acertain period before the accuracy begins to drop due to the unexpectedsignal changes. In contrast, the method described here detectssignificant changes and reacts in a timely fashion to maintain accuracy.

Experimental Set-Up: The proposed platform was deployed and evaluated ina number of residential apartments. For example, the table presented inFIG. 7 illustrates the accuracy of the real-time localization systemright after the initial training, as well as in different time periodsafter the initial training, when conducted in 7 different residentialapartments. In each round of experiments an initial training set wasrecorded, where the CSI values were captured while the user was asked towalk inside each room for 45 seconds. Also, a 45 second capture from theempty apartment was taken to train the empty or no motion class. Thenumber of classes varied from 4 to 6 in different apartments, includingthe empty or no motion class. In order to obtain examples of drift inthe input, a couple of diagnostic sets were captured in various timeintervals from 60 minutes up to 11 hours after the initial set and thesediagnostic sets were used to evaluate the adaptive algorithm. Referringto FIG. 7 , 2 to 10 rounds of evaluation per apartment were performed inorder to obtain averages for the performance results.

Experimental Results: The results presented FIG. 7 show that theaccuracy obtained by the base learner is very high (as evaluated using atest set), but using the resulting classifier over an extensive periodof time leads to significant accuracy loss. The proposed semi-supervisedlearner is able to maintain accuracy close to that of the base learnerin the face of signal drift. As explained, this is done with noadditional new labelled training data. The standard deviations indicatedare quite small.

Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments may be practiced without these specific details.For example, circuits may be shown in block diagrams in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, processes, algorithms, structures, and techniquesmay be shown without unnecessary detail in order to avoid obscuring theembodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart may describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations may be rearranged. A process is terminated when itsoperations are completed, but could have additional steps not includedin the figure. A process may correspond to a method, a function, aprocedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination corresponds to a return of the functionto the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages and/or any combination thereof. When implementedin software, firmware, middleware, scripting language and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium, such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures and/or program statements. A code segment may be coupledto another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters and/or memorycontent. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor and may vary in implementation where thememory is employed in storing software codes for subsequent execution tothat when the memory is employed in executing the software codes. Asused herein the term “memory” refers to any type of long term, shortterm, volatile, nonvolatile, or other storage medium and is not to belimited to any particular type of memory or number of memories, or typeof media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more devices for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, wireless channels and/orvarious other mediums capable of storing, containing or carryinginstruction(s) and/or data.

The methodologies described herein are, in one or more embodiments,performable by a machine which includes one or more processors thataccept code segments containing instructions. For any of the methodsdescribed herein, when the instructions are executed by the machine, themachine performs the method. Any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine are included. Thus, a typical machine may be exemplifiedby a typical processing system that includes one or more processors.Each processor may include one or more of a CPU, a graphics-processingunit, and a programmable DSP unit. The processing system further mayinclude a memory subsystem including main RAM and/or a static RAM,and/or ROM. A bus subsystem may be included for communicating betweenthe components. If the processing system requires a display, such adisplay may be included, e.g., a liquid crystal display (LCD). If manualdata entry is required, the processing system also includes an inputdevice such as one or more of an alphanumeric input unit such as akeyboard, a pointing control device such as a mouse, and so forth.

The memory includes machine-readable code segments (e.g. software orsoftware code) including instructions for performing, when executed bythe processing system, one of more of the methods described herein. Thesoftware may reside entirely in the memory, or may also reside,completely or at least partially, within the RAM and/or within theprocessor during execution thereof by the computer system. Thus, thememory and the processor also constitute a system comprisingmachine-readable code.

In alternative embodiments, the machine operates as a standalone deviceor may be connected, e.g., networked to other machines, in a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer or distributed network environment. Themachine may be, for example, a computer, a server, a cluster of servers,a cluster of computers, a web appliance, a distributed computingenvironment, a cloud computing environment, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. The term “machine” may also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The foregoing disclosure of the exemplary embodiments of the presentinvention has been presented for purposes of illustration anddescription. It is not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Many variations andmodifications of the embodiments described herein will be apparent toone of ordinary skill in the art in light of the above disclosure. Thescope of the invention is to be defined only by the claims appendedhereto, and by their equivalents.

Further, in describing representative embodiments of the presentinvention, the specification may have presented the method and/orprocess of the present invention as a particular sequence of steps.However, to the extent that the method or process does not rely on theparticular order of steps set forth herein, the method or process shouldnot be limited to the particular sequence of steps described. As one ofordinary skill in the art would appreciate, other sequences of steps maybe possible. Therefore, the particular order of the steps set forth inthe specification should not be construed as limitations on the claims.In addition, the claims directed to the method and/or process of thepresent invention should not be limited to the performance of theirsteps in the order written, and one skilled in the art can readilyappreciate that the sequences may be varied and still remain within thespirit and scope of the present invention.

What is claimed is:
 1. A method for establishing target localizationwithin an environment comprising: executing a first phase of a softwaremodel comprising an offline training phase; and executing a second phaseof the software model comprising an online evaluation and adaptationphase; wherein wireless signals employed by the software model areaccording to a predetermined standard supporting communications betweendevices disposed within the environment comprising at least a spatiallyseparated transmitter and receiver; and the online evaluation andadaptation phase comprises processing unlabeled streaming data measuredcharacteristics of the wireless signals with the configured softwaremodel to establish physical location data of another physical objectwithin the environment.
 2. The method according to claim 1, wherein theoffline training phase configures the software model using a batch oflabeled training data acquired from the environment comprising measuredcharacteristics of wireless signals and location data relating to thelocation of a physical object within the environment at the time ofmeasuring the characteristics of the wireless signals.
 3. The methodaccording to claim 1, wherein the offline training phase comprises:receiving and analyzing wireless signals and their correspondinglocation labels whilst a user is present within different location spotsof the environment; statistically formulating correlations betweenwireless signal readings and the location of the movements and eventsinside the environment through at least one algorithm of a plurality ofalgorithms, each algorithm relating to a step selected from the groupcomprising signal processing, data mining, and feature extraction;constructing a probabilistic localization model using a base classifierin dependence upon the statistically formulated correlations to generaterespective decision boundaries and a confidence score that quantifieshow certain the classifier is of its decision.
 4. The method accordingto claim 1, further comprising automatically detecting at least one of astructural shift and a drift in the distribution of streaming datacomprising: received live stream of unlabeled wireless signals arisingfrom the communications between the devices disposed within theenvironment; applying a change-point-detection technique by continuouslycomputing a divergence score; identifying the significant changes havinga score above a predefined threshold; and outputting an indicator ofdrift to at least one of the first phase of the software model, thesecond phase of the software model, another system and another process.5. The method according to claim 1, wherein the first phase of thesoftware model comprises establishing a base classifier for determininga location of a physical object within the environment in dependenceupon wireless signals between devices disposed within the environment;and the second phase of the software model comprises adapting thedecision boundaries of the base classifier with a process comprising thesteps of: establishing at least one of a shift and a drift within thewireless signals and generating a drift indicator with the baseclassifier; triggering an active query system upon generation of thedrift indication; generating a stream of confidence scores and predictedlabels from the base classifier upon generation of the drift indicator;creating a repository of high-confidence samples of the wireless signalsand their corresponding predicated labels in dependence upon the streamof confidence scores and predicted labels from the base classifier upongeneration of the drift indicator; and updating the training data of thebase classifier in dependence upon the high-confidence samples of thewireless signals and their corresponding predicated labels stored withinthe repository.
 6. A method for establishing target localization withinan environment comprising: executing a phase of the software modelcomprising an online evaluation and adaptation phase; wherein wirelesssignals employed by the software model are according to a predeterminedstandard supporting communications between devices disposed within theenvironment comprising at least a spatially separated transmitter andreceiver.
 7. The method according to claim 6, wherein the software modelhas been configured with an offline training phase using a batch oflabeled training data acquired from the environment comprising measuredcharacteristics of wireless signals and location data relating to thelocation of a physical object within the environment at the time ofmeasuring the characteristics of the wireless signals; and the onlineevaluation and adaptation phase comprises processing unlabeled streamingdata measured characteristics of the wireless signals with theconfigured software model to establish physical location data of anotherphysical object within the environment.
 8. The method according to claim6, wherein the software model has been configured in dependence upon anoffline training phase comprising: receiving and analyzing wirelesssignals and their corresponding location labels whilst a user is presentwithin different location spots of the environment; statisticallyformulating correlations between wireless signal readings and thelocation of the movements and events inside the environment through atleast one algorithm of a plurality of algorithms, each algorithmrelating to a step selected from the group comprising signal processing,data mining, and feature extraction; constructing a probabilisticlocalization model using a base classifier in dependence upon thestatistically formulated correlations to generate respective decisionboundaries and a confidence score that quantifies how certain theclassifier is of its decision.
 9. The method according to claim 6,wherein the online evaluation and adaptation phase comprises: receivinga live stream of unlabeled wireless signals without any associatedlocation indication arising from the communications between the devicesdisposed within the environment; estimating a location label for eachsegment of wireless signals using a probabilistic model built in aninitial offline training phase; and outputting final location labels toat least one of another system and process, wherein the probabilisticmodel also includes a decision-making module that applies at onestrategy of a plurality of strategies, each strategy to reduce thevariance in the sequence of predicted labels.
 10. The method accordingto claim 6, further comprising automatically detecting at least one of astructural shift and a drift in the distribution of streaming datacomprising: received live stream of unlabeled wireless signals arisingfrom the communications between the devices disposed within theenvironment; applying a change-point-detection technique by continuouslycomputing a divergence score; identifying the significant changes havinga score above a predefined threshold; and outputting an indicator ofdrift to at least one of another phase of the software model whichconfigures the software model, the phase of the software model, anothersystem and another process.
 11. The method according to claim 6, whereinthe software model has been configured in dependence upon an offlinetraining phase comprising: establishing a base classifier fordetermining a location of a physical object within the environment independence upon wireless signals between devices disposed within theenvironment; and the phase of the software model comprises adapting thedecision boundaries of the base classifier with a process comprising thesteps of: establishing at least one of a shift and a drift within thewireless signals and generating a drift indicator with the baseclassifier; triggering an active query system upon generation of thedrift indication; generating a stream of confidence scores and predictedlabels from the base classifier upon generation of the drift indicator;creating a repository of high-confidence samples of the wireless signalsand their corresponding predicated labels in dependence upon the streamof confidence scores and predicted labels from the base classifier upongeneration of the drift indicator; and updating the training data of thebase classifier in dependence upon the high-confidence samples of thewireless signals and their corresponding predicated labels stored withinthe repository.
 12. The method according to claim 6, wherein theenvironment is an indoor environment; and the first phase of thesoftware model comprises establishing an initial room-level andsub-room-level localization model by employing a base classifier uponmetrics extracted from the wireless signals and corresponding referencelabels of the regions and sub-regions of the environment.
 13. The methodaccording to claim 6, wherein the online evaluation and adaptation phasecomprises processing unlabeled streaming data measured characteristicsof the wireless signals with the configured software model to establishphysical location data of another physical object within theenvironment.
 14. The method according to claim 6, wherein the phase ofthe software model further comprises adapting the decision boundaries ofthe base classifier with a process comprising the steps of: establishingat least one of a shift and a drift within the wireless signals andgenerating a drift indicator with a base classifier; triggering anactive query system upon generation of the drift indication; generatinga stream of confidence scores and predicted labels from the baseclassifier upon generation of the drift indicator; creating a repositoryof high-confidence samples of the wireless signals and theircorresponding predicated labels in dependence upon the stream ofconfidence scores and predicted labels from the base classifier upongeneration of the drift indicator; and updating the training data of thebase classifier in dependence upon the high-confidence samples of thewireless signals and their corresponding predicated labels stored withinthe repository; and the base classifier determines a location of aphysical object within the environment in dependence upon wirelesssignals between devices disposed within the environment.
 15. The methodaccording to claim 6, wherein the software model was established independence upon processing extracted wireless signals with a trainingprocess whilst reference labels of the regions and sub-regions of theenvironment containing motion and physical movement of a moving subjectare auto-generated established based on behavioral statistics of thewireless signals.
 16. The method according to claim 6, wherein at leastone of: the software model comprises a stabilization process comprisingat least one of: executing a plurality of decision-making strategies,each strategy relating to a mathematical technique to establish at leastone of a determined location and a confidence score relating to apredicted location label wherein the plurality of decision-makingstrategies are employed to improve the stability of the software model;and executing a change-point-detection process to compute a divergencescore and employing the divergence score to identify significant changesin metrics extracted from the wireless signals the software modelcomprises an active query system which executes a process comprising:establishing real time divergence scores relating to metrics extractedfrom the wireless signals between the devices within the environmentwhich are processed to provide localization information relating to anobject within the predetermined indoor region; and establishing arepository of high-confidence examples of the extracted metrics andtheir corresponding high-confidence location reference for use by thesecond phase of the software model.
 17. The method according to claim 6,wherein at least one of: the software model executes an auto-adaptationmethod to update decision boundaries of an initial training localizationmodel of regions and sub-regions of the environment wherein theauto-adaptation method employs a base classifier and data stored withina high-confidence repository and the data stored within thehigh-confidence repository was established in dependence upon an activequery process in execution upon the processor processing generatedreal-time divergence scores on the extracted metrics of the wirelesssignals to establish examples of high-confidence wireless metrics andtheir corresponding high-confidence location reference; and the softwaremodel a data preparation module comprising processes for: noise removalwherein raw data relating to channel state information (CSI) of thewireless signals is filtered with a set of digital filters where eachfilter of the set of digital filters the raw data over a predeterminedfrequency range associated with a target moving activity to be detectedby the software model; standardization wherein a fixed score scalingnormalization process is applied to standardize the CSI feature spade toa predetermined reference range; and feature extraction comprising: afirst step a sliding window is employed to each stream of CSI samples toextract correlated features that describe a location of an event withinthe environment; and a second step of extracting or generating newfeature spaces from the first step through combining multiple domaininformation.
 18. The method according to claim 6, wherein the softwaremodel incorporates a decision making process employing a classificationprocess wherein a final class decision for a current decision at a pointin time the classification process comprises the steps of: discardingrare class labels that last less than a consecutive samples; discardingany class label with a confidence score less than β; and imposing anextra bias towards keeping the current predicted class label until theaverage confidence score for switching to another class reaches acertain level \gamma; and α, β, and γ are empirically established by thesecond phase of the software model over a period of time.
 19. A methodfor establishing target localization within an environment comprising:executing a first phase of a software model comprising an offlinetraining phase; and executing a second phase of the software modelcomprising an online evaluation and adaptation phase; wherein wirelesssignals employed by the software model are according to a predeterminedstandard supporting communications between devices disposed within theenvironment comprising at least a spatially separated transmitter andreceiver; the online evaluation and adaptation phase comprises:receiving a live stream of unlabeled wireless signals without anyassociated location indication arising from the communications betweenthe devices disposed within the environment; estimating a location labelfor each segment of wireless signals using a probabilistic model builtin an initial offline training phase; and outputting final locationlabels to at least one of another system and process, and theprobabilistic model also includes a decision-making module that appliesat one strategy of a plurality of strategies, each strategy to reducethe variance in the sequence of predicted labels.
 20. A method forestablishing target localization within an environment comprising:executing a first phase of a software model comprising an offlinetraining phase; and executing a second phase of the software modelcomprising an online evaluation and adaptation phase; wherein wirelesssignals employed by the software model are according to a predeterminedstandard supporting communications between devices disposed within theenvironment comprising at least a spatially separated transmitter andreceiver; the environment is an indoor environment; and the first phaseof the software model comprises establishing an initial room-level andsub-room-level localization model by employing a base classifier uponmetrics extracted from the wireless signals and corresponding referencelabels of the regions and sub-regions of the environment.